scholarly journals Prediction of Solid Waste Generation Rates in Urban Region of Laos Using Socio-Demographic and Economic Parameters with a Multi Linear Regression Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 3038
Author(s):  
Kanchan Popli ◽  
Chunkyoo Park ◽  
Sang-Min Han ◽  
Seungdo Kim
Keyword(s):  
Linear Regression ◽  
Solid Waste ◽  
Urban Poverty ◽  
Gdp Per Capita ◽  
Waste Generation ◽  
Economic Parameters ◽  
Urban Solid Waste ◽  
Solid Waste Generation ◽  
Multi Linear Regression ◽  
Per Capita

This paper aims to develop a predictive model for Laos to generate reliable statistics for urban solid waste from 1995 to 2050. The multi-linear regression (MLR) approach is used with six different socio-demographic and economic parameters, i.e., urban population, gross domestic product (GDP) per capita, urban literacy rate, urban poverty incidence, urban household size and urban unemployment rate. Different reliable models are generated under four different scenarios. The value of R2 (a relative measure of fit) and value of performance indicators (an absolute measure of fit) such as mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are calculated to assure the validity and accuracy of the results. Model 2 of Scenario 4 is estimated as the best model, where population and GDP per capita show statistical significance for estimating urban solid waste generation rate in Laos. The amount of municipal solid waste is estimated to be 0.98 million tons (MT) in the year 2030, 1.26 MT in the year 2040 and 1.52 MT in the year 2050, assuming that the present waste generation trends will be followed in the future. Moreover, the study provides an easy and detailed explanation of the work which will increase the interest of researchers, allow them to understand the MLR approach clearly and inspire them to use it for other developing countries where the scarcity of data is a major obstacle in the field of solid waste management. The drawback of the study is the limited availability of historical official and reliable data statistics in Laos for the dependent and independent variables.

scholarly journals The Impact of Economic Class on Solid Waste Generation Pattern in Capital City of Kuala Lumpur, Malaysia

2019 ◽  
Vol 8 (6) ◽  
pp. 4807-4812
Keyword(s):  
Waste Management ◽  
Solid Waste ◽  
Household Income ◽  
Solid Waste Management ◽  
Low Cost ◽  
Gdp Per Capita ◽  
Waste Generation ◽  
Kuala Lumpur ◽  
Solid Waste Generation ◽  
Per Capita

Solid waste management has been recognized as the crucial urban environmental problem in Malaysia. In order to ameliorate waste management, it is essential to know the quantity of waste generated and the elements contributing to the generation of waste. This study was conducted to determine the association between socioeconomic factors and solid waste generation in Kuala Lumpur, to analyse the class of income that contribute the highest contribution of solid waste generation and to propose mitigation plan to local authority in controlling waste production in the study area. In this paper, the socioeconomic factors that lead to waste generation in Kuala Lumpur are presented. A seventeen years (2000-2017) secondary database obtained from the Department of Statistic Malaysia (DOSM), National Solid Waste Management Department (NSWMD) and National Property Information Centre (NAPIC) were statistically analysed by using Chemometric techniques which includes Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). The study revealed that population, household income, GDP per capita, low cost flat, 2-3 storey terraced, town house, 2-3 storey semi-detached and apartment/condominium were positively strong correlated with solid waste generation. PCA analysis indicated that population, household income, GDP per capita, single storey terraced, 2-3 storey terraced, single storey semi-detached, 2-3 storey semi-detached, town house, low cost flat, and condominium/apartment were the most potential contributors to waste generation. MLR revealed that population, GDP per capita, household income, high income class and middle-income class were the significant factor towards solid waste generation in Kuala Lumpur. This study gives new insight on the part of socioeconomic parameter in influencing the production of solid waste.

scholarly journals Application of Modified Grey Forecasting Model to Predict the Municipal Solid Waste Generation using MLP and MLE

2021 ◽  
Vol 6 (5) ◽  
pp. 1276-1296
Author(s):  
Mohd Anjum ◽  
Sana Shahab ◽  
Mohammad Sarosh Umar
Keyword(s):  
Linear Regression ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Likelihood Estimation ◽  
Grey Model ◽  
Waste Generation ◽  
Elementary Model ◽  
Grey Forecasting ◽  
Solid Waste Generation ◽  
Forecasting Theory

Grey forecasting theory is an approach to build a prediction model with limited data to produce better forecasting results. This forecasting theory has an elementary model, represented as the GM(1,1) model , characterized by the first-order differential equation of one variable. It has the potential for accurate and reliable forecasting without any statistical assumption. The research proposes a methodology to derive the modified GM(1,1) model with improved forecasting precision. The residual series is forecasted by the GM(1,1) model to modify the actual forecasted values. The study primarily addresses two fundamental issues: sign prediction of forecasted residual and the procedure for formulating the grey model. Accurate sign prediction is very complex, especially when the model lacks in data. The signs of forecasted residuals are determined using a multilayer perceptron to overcome this drawback. Generally, the elementary model is formulated conventionally, containing the parameters that cannot be calculated straightforward. Therefore, maximum likelihood estimation is incorporated in the modified model to resolve this drawback. Three statistical indicators, relative residual, posterior variance test, and absolute degree of grey indices, are evaluated to determine the model fitness and validation. Finally, an empirical study is performed using actual municipal solid waste generation data in Saudi Arabia, and forecasting accuracies are compared with the linear regression and original GM(1,1). The MAPEs of all models are rigorously examined and compared, and then it is obtained that the forecasting precision of GM(1,1) model , modified GM(1,1) model, and linear regression is 15.97%, 8.90%, and 27.90%, respectively. The experimental outcomes substantiate that the modified grey model is a more suitable forecasting approach than the other compared models.

Urban solid waste generation and disposal in Mexico: a case study

2001 ◽  
Vol 19 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Otoniel Buenrostro ◽  
Gerardo Bocco ◽  
Gerardo Bernache
Keyword(s):  
Solid Waste ◽  
Waste Generation ◽  
Urban Solid Waste ◽  
Solid Waste Generation

Environmental sustainability features in large university campuses

2019 ◽  
Vol 20 (2) ◽  
pp. 214-228 ◽  
Author(s):  
Hani Abu Qdais ◽  
Osama Saadeh ◽  
Mohamad Al-Widyan ◽  
Raed Al-tal ◽  
Muna Abu-Dalo
Keyword(s):  
Solid Waste ◽  
Water Consumption ◽  
Carbon Emission ◽  
University Campus ◽  
Waste Generation ◽  
Content Type ◽  
University Campuses ◽  
Solid Waste Generation ◽  
Per Capita ◽  
The University

Purpose The purpose of this study is to describe the efforts undertaken to convert the large university campus of Jordan University of Science and Technology (JUST) into a green, resource-efficient and low-carbon campus by following an action-oriented strategy. Sustainability features of the campus were discussed and benchmarked. Challenges were identified and remedial actions were proposed. Design/methodology/approach Taking 2015 as the baseline year, data on energy, water consumption and solid waste generation for the university campus were collected. Energy consumption for cooling, heating and transportation, besides electric power consumption, were reported, and the associated carbon dioxide (CO2) emissions were estimated. By calculating the full time equivalent of students and employees, carbon emission and water consumption per capita were calculated. A comparison with other universities worldwide was conducted. Findings Although located in a semiarid region with scarce water resources, JUST has set an example by greening its campus through an action-oriented approach. It was found that the per capita carbon emission for JUST campus was 1.33 ton of CO2 equivalent, which is less than the emissions from campuses of other universities worldwide. As for water, this study revealed that the daily per capita water consumption was about 56 L, which is approximately one-third of that for students in institutions in the USA. Furthermore, the findings of this study indicated that the average solid waste generation rate was 0.37 kg per student per day compared to 0.31 kg per capita per day when considering the university community (students and employees) collectively. These figures were less and thus compare favorably to the corresponding data for other universities in both developing and developed countries. Originality/value This research addresses the issue of greening JUST campus, which is one of the largest university campuses in the world. JUST campus is located in a semiarid, water-scarce country, which on its own poses a serious challenge. The originality and value of this study mainly stem from the facts that on the one hand, this is one of the unique and pioneering comprehensive studies of its type and, on the other hand, other universities with similar conditions can benefit from the findings of this research to meet the sustainability objectives on their campus operations.

scholarly journals Cities and Greenhouse Gas Emissions: Moving Forward

Urbanisation ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 43-62
Author(s):  
Daniel Hoornweg ◽  
Lorraine Sugar ◽  
Claudia Lorena Trejos Gomez
Keyword(s):  
Greenhouse Gas ◽  
Solid Waste ◽  
Policy Development ◽  
Urban Poor ◽  
Ghg Emissions ◽  
Waste Generation ◽  
Large Cities ◽  
Production And Consumption ◽  
Solid Waste Generation ◽  
Per Capita

Cities are blamed for the majority of greenhouse gas (GHG) emissions. So too are more affluent, highly urbanised countries. If all production- and consumption-based emissions that result from lifestyle and purchasing habits are included, urban residents and their associated affluence likely account for more than 80 per cent of the world’s GHG emissions. Attribution of GHG emissions should be refined. Apportioning responsibility can be misguided, as recent literature demonstrates that residents of denser city centres can emit half the GHG emissions of their suburban neighbours. It also fails to capture the enormous disparities within and across cities as emissions are lowest for poor cities and particularly low for the urban poor. This article presents a detailed analysis of per capita GHG emissions for several large cities and a review of per capita emissions for 100 cities for which peer-reviewed studies are available. This highlights how average per capita GHG emissions for cities vary from more than 15 tonnes of carbon dioxide equivalent (tCO2e) (Sydney, Calgary, Stuttgart and several major US cities) to less than half a tonne (various cities in Nepal, India and Bangladesh). The article discusses where GHG emissions arise and where mitigation efforts may be most effective. It illustrates the need to obtain comparable estimates at city level and the importance of defining the scope of the analysis. Emissions for Toronto are presented at a neighbourhood level, city core level and metropolitan area level, and these are compared with provincial and national per capita totals. This shows that GHG emissions can vary noticeably for the same resident of a city or country, depending on whether these are production- or consumption-based values. The methodologies and results presented form important inputs for policy development across urban sectors. The article highlights the benefits and drawbacks of apportioning GHG emissions (and solid waste generation) per person. A strong correlation between high rates of GHG emissions and solid waste generation is presented. Policies that address both in concert may be more effective as they are both largely by-products of lifestyles.

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